What is GPT-4.5, and how is it different from a reasoning model?

Yesterday, Thursday, February 27, 2025, OpenAI released GPT-4.5, a model it describes as its largest and best yet for conversation. Reports had referred to it by its code name, Orion. It is labeled a research preview: OpenAI wants to learn what the model is good at from how people use it, and it says so openly.

GPT-4.5 is interesting less for any single score than for what it tests. For years, each new GPT was built mainly by making the model bigger and feeding it more data. Recently, the industry's attention moved to reasoning models that spend extra time thinking before they answer. GPT-4.5 is the newest large bet on the older recipe.

What OpenAI released

The model reaches people in stages. ChatGPT Pro subscribers, on the $200-a-month plan, can choose it in the model menu on the web, phone and desktop apps starting yesterday. OpenAI says Plus and Team users follow next week, then Enterprise and Edu the week after.

In ChatGPT, GPT-4.5 can search the web, read files and images you upload, and work in canvas, the side-by-side editor for writing and code. It does not yet support Voice Mode, video or screen sharing.

Developers on any paid tier of OpenAI's API can try it through the Chat Completions, Assistants and Batch APIs. It supports function calling (asking your app to run an action), Structured Outputs (replies in an exact data format), streaming, system messages and image input.

It is expensive. TechCrunch reports API prices of $75 per million input tokens and $150 per million output tokens, against $2.50 and $10 for GPT-4o. (A token is a piece of a word; a million is roughly 750,000 words.) OpenAI says GPT-4.5 does not replace GPT-4o, and it is still deciding whether to keep offering it in the API over the long term.

Two ways to make a model smarter

OpenAI describes two separate routes to better models.

The first is pre-training, also called unsupervised learning: the model reads an enormous amount of text and learns to predict what comes next. Make the model larger, give it more data and more computing power, and it picks up more knowledge and a better feel for language. GPT-1 through GPT-4 were all built mainly this way, and GPT-4.5 continues the line. OpenAI trained it on Microsoft Azure AI supercomputers.

The second is reasoning: training a model to produce a chain of thought, a hidden step-by-step working-out, before it replies. OpenAI's o1 and o3-mini follow this route, and it pays off on math, science and logic puzzles.

An analogy helps. Pre-training is like reading widely for years: you build a large store of knowledge and intuition. Reasoning is like taking your time on an exam: you work through each step carefully. GPT-4.5 is the well-read friend who answers from what they know, not the one who reaches for scrap paper.

OpenAI also says it used new training techniques in which smaller models help produce data for larger ones, to make GPT-4.5 better at grasping what people mean.

What the numbers show

OpenAI published these results comparing GPT-4.5 with GPT-4o and o3-mini (set to think hard):

The pattern is clear. GPT-4.5 beats GPT-4o everywhere and leads on broad knowledge across languages, but the reasoning model wins easily on math and hard science. TechCrunch notes that GPT-4.5 also trails other reasoning models, such as DeepSeek's R1 and Anthropic's Claude 3.7 Sonnet, on those tests.

Where OpenAI sees the gains is in things scores capture poorly. On SimpleQA, a set of short factual questions, GPT-4.5 is more accurate than GPT-4o, o1 and o3-mini, and it invents answers less often. OpenAI says testers preferred its replies to GPT-4o's, and that it reads tone and unspoken expectations better, which it calls higher "EQ". In one example, when a user says they failed a test, GPT-4.5 offers a short, warm reply and asks whether they want to talk, where GPT-4o lists six pieces of advice.

Why it matters

GPT-4.5 is a large, costly experiment, and many observers read it as a sign that simply scaling up pre-training is bringing smaller jumps than before. OpenAI's own view is that the two routes work together: a model with broader knowledge should become a stronger foundation for future reasoning models and agents.

For everyday users, the takeaway is practical. "Newest" no longer means "best at everything." Different models now have different strengths, and choosing well is becoming part of using AI well.

When should you pick GPT-4.5 over a reasoning model?

Pick GPT-4.5 when the task depends on knowledge, tone and judgment more than on step-by-step logic. OpenAI suggests writing help, communication, coaching, learning and brainstorming. Examples: rewording a difficult message so it sounds kind, explaining a topic in plain terms, or giving feedback on a draft. Its broader knowledge and lower rate of invented facts help here, though you should still check anything important.

Pick a reasoning model such as o1 or o3-mini when the task has a right answer that takes several steps to reach: a math problem, a tricky piece of code, a schedule with many constraints. The table above shows how large the gap can be.

Cost and access matter too. GPT-4.5 is limited to Pro users for now and is far more expensive in the API, so builders should test it on their own examples before committing. Here is a prompt that plays to its strengths:

I need to tell my volunteer team that our spring event is cancelled.
Write a short message that is honest, warm and thanks them for their work.
Keep it under 120 words and avoid sounding like a corporate notice.